Explore the vast range of titles available.

Epithelial ovarian cancer (EOC) is one of the most prevalent gynaecological cancers worldwide. The molecular mechanisms of serous ovarian cancer (SOC) remain unclear and not well understood. SOC cases are primarily diagnosed at the late stage, resulting in a poor prognosis. Advances in molecular biology techniques allow us to obtain a better understanding of precise molecular mechanisms and to identify the chromosome instability region and key driver genes in the carcinogenesis and progression of SOC. Whole‐exome sequencing was performed on the normal ovarian cell line IOSE80 and the EOC cell lines SKOV3 and A2780. The single‐nucleotide variation burden, distribution, frequency and signature followed the known ovarian mutation profiles, without chromosomal bias. Recurrently mutated ovarian cancer driver genes, including LRP1B, KMT2A, ARID1A, KMT2C and ATRX were also found in two cell lines. The genome distribution of copy number alterations was found by copy number variation (CNV) analysis, including amplification of 17q12 and 4p16.1 and deletion of 10q23.33. The CNVs of MED1, GRB7 and MIEN1 located at 17q12 were found to be correlated with the overall survival of SOC patients (MED1: p = 0.028, GRB7: p = 0.0048, MIEN1: p = 0.0051), and the expression of the three driver genes in the ovarian cell line IOSE80 and EOC cell lines SKOV3 and A2780 was confirmed by western blot and cell immunohistochemistry.

As the COVID‐19 pandemic evolves and new variants emerge, the development of more efficient identification approaches of variants is urgent to prevent continuous outbreaks of SARS‐CoV‐2. Field‐effect transistors (FETs) with two‐dimensional (2D) materials are viable platforms for the detection of virus nucleic acids (NAs) but cannot yet provide accurate information on NA variations. Herein, 2D Indium selenide (InSe) FETs were used to identify SARS‐CoV‐2 variants. The device's mobility and stability were ensured by atomic layer deposition (ALD) of Al2O3. The resulting FETs exhibited sub‐fM detection limits ranging from 10–14  M to 10–8 M. The recognition of single‐nucleotide variations was achieved within 15 min to enable the fast and direct identification of two core mutations (L452R, R203M) in Delta genomes (p < 0.01). Such capability originated from the trap states in oxidized InSe (InSe1−xOx) after ALD, resulting in traps‐involved carrier transport responsive to the negative charges of NAs. In sum, the proposed approach might highly provide epidemiological information for timely surveillance of the COVID pandemic. Currently, transmissible SARS‐CoV‐2 variants continuously evolve due to the frequent occurrence of single‐nucleotide variations (SNVs). We develop 2D InSe FETs for fast and direct identification of SARS‐CoV‐2 variants. In addition to the sub‐fM detection limit, the bio‐FETs can directly recognize the difference between complementary and Delta variant nucleic acid sequences with SNVs. We contribute the capability to the oxidation of InSe (InSe1−xOx). Moreover, the trap states inside make the carrier transport of our bio‐FETs more sensitive to the EF shift from the negative charges of NA sequences.

The stromal antigen 3 (STAG3) gene, encoding a meiosis-specific cohesin component, is a strong candidate for causing male infertility, but little is known about this gene so far. We identified STAG3 in patients with nonobstructive azoospermia (NOA) and normozoospermia in the Korean population. The coding regions and their intron boundaries of STAG3 were identified in 120 Korean men with spermatogenic impairments and 245 normal controls by using direct sequencing and haplotype analysis. A total of 30 sequence variations were identified in this study. Of the total, seven were exonic variants, 18 were intronic variants, one was in the 5'-UTR, and four were in the 3'-UTR. Pathogenic variations that directly caused NOA were not identified. However, two variants, c.3669+35C>G (rs1727130) and +198A>T (rs1052482), showed significant differences in the frequency between the patient and control groups (P = 0.021, odds ratio [OR]: 1.79, 95% confidence interval [CI]: 1.098-2.918) and were tightly linked in the linkage disequilibrium (LD) block. When pmir-rs1052482A was cotransfected with miR-3162-5p, there was a substantial decrease in luciferase activity, compared with pmir-rs1052482T. This result suggests that rs1052482 was located within a binding site of miR-3162-5p in the STAG3 3'-UTR, and the minor allele, the rs1052482T polymorphism, might offset inhibition by miR-3162-5p. We are the first to identify a total of 30 single-nucleotide variations (SNVs) of STAG3 gene in the Korean population. We found that two SNVs (rs1727130 and rs1052482) located in the 3'-UTR region may be associated with the NOA phenotype. Our findings contribute to understanding male infertility with spermatogenic impairment.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of COVID 19, continues to evolve since its first emergence in December 2019. Using the complete sequences of 1,932 SARS-CoV-2 genomes, various clustering analyses consistently identified six types of the strains. Independent of the dendrogram construction, 13 signature variations in the form of single nucleotide variations (SNVs) in protein coding regions and one SNV in the 5′ untranslated region (UTR) were identified and provided a direct interpretation for the six types (types I to VI). The six types of the strains and their underlying signature SNVs were validated in two subsequent analyses of 6,228 and 38,248 SARS-CoV-2 genomes which became available later. To date, type VI, characterized by the four signature SNVs C241T (5′UTR), C3037T (nsp3 F924F), C14408T (nsp12 P4715L), and A23403G (Spike D614G), with strong allelic associations, has become the dominant type. Since C241T is in the 5′ UTR with uncertain significance and the characteristics can be captured by the other three strongly associated SNVs, we focus on the other three. The increasing frequency of the type VI haplotype 3037T-14408T-23403G in the majority of the submitted samples in various countries suggests a possible fitness gain conferred by the type VI signature SNVs. The fact that strains missing one or two of these signature SNVs fail to persist implies possible interactions among these SNVs. Later SNVs such as G28881A, G28882A, and G28883C have emerged with strong allelic associations, forming new subtypes. This study suggests that SNVs may become an important consideration in SARS-CoV-2 classification and surveillance.

We present biallelic SNVs called from 2,548 samples across 26 populations from the 1000 Genomes Project, called directly on GRCh38. We believe this will be a useful reference resource for those using GRCh38, representing an improvement over the “lift-overs” of the 1000 Genomes Project data that have been available to date and providing a resource necessary for the full adoption of GRCh38 by the community. Here, we describe how the call set was created and provide benchmarking data describing how our call set compares to that produced by the final phase of the 1000 Genomes Project on GRCh37.

Allele-specific DNA methylation (ASM) provides critical insights into the complex genetic and epigenetic mechanisms regulating gene transcription. Emerging evidence suggests that ASM is particularly enriched in gene enhancer regions, and recent studies have demonstrated that ASM is increased in cancer tissues compared with normal tissues. Despite the increasing recognition of ASM as a potential biomarker in tumorigenesis, systematic resources dedicated to identifying and annotating ASMs in cancer contexts remain limited. In this study, we developed CanASM ( https://bioinfor.nefu.edu.cn/CanASM/ ), the first comprehensive database specifically designed to identify and annotate ASM in cancer. In CanASM, ASM sites identified from bisulfite sequencing (BS-Seq) data across 31 cancer types and their matched normal tissue samples are cataloged. Importantly, CanASM includes extensive regulatory annotations for ASMs, including associated genes, cis-regulatory elements and transcription factor binding colocalizations, transcription factor affinity changes, etc. Users can query and explore ASMs using various parameters, such as single-nucleotide variations (SNVs), chromosomal coordinates, and gene names. The current version of CanASM includes 5,003,877 unique SNV–CpG pairs, including 3,056,776 index SNVs, of which 2,634,406 are single-nucleotide polymorphisms (SNPs), and 4,157,508 CpGs. With an intuitive interface for browsing, querying, analyzing, and downloading, CanASM serves as a valuable resource for researchers investigating cancer-associated genetic variations and epigenetic regulation in cancer.

G-quadruplexes (G4s) formed by guanine-rich nucleic acids play a role in essential biological processes such as transcription and replication. Besides the >1.5 million putative G-4–forming sequences (PQSs), the human genome features >640 million single-nucleotide variations (SNVs), the most common type of genetic variation among people or populations. An SNV may alter a G4 structure when it falls within a PQS motif. To date, genome-wide PQS–SNV interactions and their impact have not been investigated. Herein, we present a study on the PQS–SNV interactions and the impact they can bring to G4 structures and, subsequently, gene expressions. Based on build 154 of the Single Nucleotide Polymorphism Database (dbSNP), we identified 5 million gains/losses or structural conversions of G4s that can be caused by the SNVs. Of these G4 variations (G4Vs), 3.4 million are within genes, resulting in an average load of >120 G4Vs per gene, preferentially enriched near the transcription start site. Moreover, >80% of the G4Vs overlap with transcription factor–binding sites and >14% with enhancers, giving an average load of 3 and 7.5 for the two regulatory elements, respectively. Our experiments show that such G4Vs can significantly influence the expression of their host genes. These results reveal genome-wide G4Vs and their impact on gene activity, emphasizing an understanding of genetic variation, from a structural perspective, of their physiological function and pathological implications. The G4Vs may also provide a unique category of drug targets for individualized therapeutics, health risk assessment, and drug development.

An incomplete ascertainment of genetic variation within the highly polymorphic immunoglobulin heavy chain locus (IGH) has hindered our ability to define genetic factors that influence antibody-mediated processes. Due to locus complexity, standard high-throughput approaches have failed to accurately and comprehensively capture IGH polymorphism. As a result, the locus has only been fully characterized two times, severely limiting our knowledge of human IGH diversity. Here, we combine targeted long-read sequencing with a novel bioinformatics tool, IGenotyper, to fully characterize IGH variation in a haplotype-specific manner. We apply this approach to eight human samples, including a haploid cell line and two mother-father-child trios, and demonstrate the ability to generate high-quality assemblies (>98% complete and >99% accurate), genotypes, and gene annotations, identifying 2 novel structural variants and 15 novel IGH alleles. We show multiplexing allows for scaling of the approach without impacting data quality, and that our genotype call sets are more accurate than short-read (>35% increase in true positives and >97% decrease in false-positives) and array/imputation-based datasets. This framework establishes a desperately needed foundation for leveraging IG genomic data to study population-level variation in antibody-mediated immunity, critical for bettering our understanding of disease risk, and responses to vaccines and therapeutics.

μ-opioid receptor (MOP) plays a critical role in mediating opioid analgesic effects. Genetic variations, particularly those in the MOP gene (Oprm1), significantly influence individual variations in opioid efficacy and side effects across species, highlighting the need for pharmacogenomic research in human and veterinary contexts. This study aimed to identify single-nucleotide variations (SNVs) within Oprm1 in 100 cats of various breeds. Oprm1 spans over 170 kb and consists of five exons that combine to yield three splice variants in the cat Ensembl database. Among these variants, Oprm1-202 is an ortholog of the MOR-1 transcript, which is the most abundant in humans and mice. Oprm1-202 shares 92% and 87% coding sequences (CDS) and 96% and 94% amino acid sequence identity with human and mouse MOR-1, respectively. Phylogenetic trees were constructed from the CDS and amino acid sequences of nine species, including humans, cats, and mice. Both the CDS and amino acid sequences of MOP in cats showed phylogenetic development closer to that of primates than of rodents. Four SNVs were identified in the CDS of Oprm1. One SNV was located in exon 1 and the other three in exon 2 of Oprm1, all of which were synonymous substitutions. Although synonymous mutations generally have a limited functional impact, they may influence splicing and receptor expression. Further research is required to assess the effects of these SNVs on opioid efficacy, receptor expression, and analgesic responses across breeds, considering the potential breed-specific genetic factors in cat species.

Exon splicing triggered by unpredicted genetic mutation can cause translational variations in neurodegenerative disorders. In this study, we discover Alzheimer’s disease (AD)-specific single-nucleotide variants (SNVs) and abnormal exon splicing of phospholipase c gamma-1 (PLCγ1) gene, using genome-wide association study (GWAS) and a deep learning-based exon splicing prediction tool. GWAS revealed that the identified single-nucleotide variations were mainly distributed in the H3K27ac-enriched region of PLCγ1 gene body during brain development in an AD mouse model. A deep learning analysis, trained with human genome sequences, predicted 14 splicing sites in human PLCγ1 gene, and one of these completely matched with an SNV in exon 27 of PLCγ1 gene in an AD mouse model. In particular, the SNV in exon 27 of PLCγ1 gene is associated with abnormal splicing during messenger RNA maturation. Taken together, our findings suggest that this approach, which combines in silico and deep learning-based analyses, has potential for identifying the clinical utility of critical SNVs in AD prediction.